An integrated scheme for dynamic security assessment considering misclassification constraint based on umbrella Neyman-Pearson classifiers

Abstract Power system is one of the most complex dynamic industrial systems with the continuous expansion of the system scale and extensive application of phasor measurement units (PMUs). Dynamic security assessment (DSA) misclassification of the system will bring unpredictable consequences. Therefore, an integrated scheme for DSA that considers misclassification constraint is proposed in this paper to satisfy practical applications. The scheme includes a feature selection process and a DSA model, which are based on the bagging nearest-neighbor prediction independence test (BNNPT) and umbrella Neyman-Pearson (NP) classifiers, respectively. The BNNPT can explore the relationships between variables and the dynamic security index (DSI) to screen out the crucial features highly related to the DSI. By controlling the Type I error threshold and the cyclic split training mode, umbrella NP classifiers can effectively constrain Type I error, reduce the effect of misclassification, and improve DSA for the secure operation of the system. The performance of this integrated scheme is demonstrated through tests on the IEEE 39-bus system and a practical 500-bus system.